Initial findings using high-resolution magnetic resonance imaging for visualisation of the sural nerve and surrounding anatomy in healthy volunteers at 7 Tesla.

BACKGROUND AND AIMS: Histopathological diagnosis is the gold standard in many acquired inflammatory, infiltrative and amyloid based peripheral nerve diseases and a sensory nerve biopsy of sural or superficial peroneal nerve is favoured where a biopsy is deemed necessary. The ability to determine nerve pathology by high-resolution imaging techniques resolving anatomy and imaging characteristics might improve diagnosis and obviate the need for biopsy in some. The sural nerve is anatomically variable and occasionally adjacent vessels can be sent for analysis in error. Knowing the exact position and relationships of the nerve prior to surgery could be clinically useful and thus reliably resolving nerve position has some utility. METHODS: 7T images of eight healthy volunteers' (HV) right ankle were acquired in a pilot study using a double-echo in steady-state sequence for high-resolution anatomy images. Magnetic Transfer Ratio images were acquired of the same area. Systematic scoring of the sural, tibial and deep peroneal nerve around the surgical landmark 7 cm from the lateral malleolus was performed (number of fascicles, area in voxels and mm2, diameter and location relative to nearby vessels and muscles). RESULTS: The sural and tibial nerves were visualised in the high-resolution double-echo in steady-state (DESS) image in all HV. The deep peroneal nerve was not always visualised at level of interest. The MTR values were tightly grouped except in the sural nerve where the nerve was not visualised in two HV. The sural nerve location was found to be variable (e.g., lateral or medial to, or crossing behind, or found positioned directly posterior to the saphenous vein). INTERPRETATION: High-resolution high-field images have excellent visualisation of the sural nerve and would give surgeons prior knowledge of the position before surgery. Basic imaging characteristics of the sural nerve can be acquired, but more detailed imaging characteristics are not easily evaluable in the very small sural and further developments and specific studies are required for any diagnostic utility at 7T.

Sequence-agnostic motion-correction leveraging efficiently calibrated Pilot Tone signals.

PURPOSE: This study leverages externally generated Pilot Tone (PT) signals to perform motion-corrected brain MRI for sequences with arbitrary k-space sampling and image contrast. THEORY AND METHODS: PT signals are promising external motion sensors due to their cost-effectiveness, easy workflow, and consistent performance across contrasts and sampling patterns. However, they lack robust calibration pipelines. This work calibrates PT signal to rigid motion parameters acquired during short blocks (˜4 s) of motion calibration (MC) acquisitions, which are short enough to unobstructively fit between acquisitions. MC acquisitions leverage self-navigated trajectories that enable state-of-the-art motion estimation methods for efficient calibration. To capture the range of patient motion occurring throughout the examination, distributed motion calibration (DMC) uses data acquired from MC scans distributed across the entire examination. After calibration, PT is used to retrospectively motion-correct sequences with arbitrary k-space sampling and image contrast. Additionally, a data-driven calibration refinement is proposed to tailor calibration models to individual acquisitions. In vivo experiments involving 12 healthy volunteers tested the DMC protocol's ability to robustly correct subject motion. RESULTS: The proposed calibration pipeline produces pose parameters consistent with reference values, even when distributing only six of these approximately 4-s MC blocks, resulting in a total acquisition time of 22 s. In vivo motion experiments reveal significant ( p < 0.05 $$ p<0.05 $$ ) improved motion correction with increased signal to residual ratio for both MPRAGE and SPACE sequences with standard k-space acquisition, especially when motion is large. Additionally, results highlight the benefits of using a distributed calibration approach. CONCLUSIONS: This study presents a framework for performing motion-corrected brain MRI in sequences with arbitrary k-space encoding and contrast, using externally generated PT signals. The DMC protocol is introduced, promoting observation of patient motion occurring throughout the examination and providing a calibration pipeline suitable for clinical deployment. The method's application is demonstrated in standard volumetric MPRAGE and SPACE sequences.

High resolution and contrast 7 tesla MR brain imaging of the neonate.

Introduction: Ultra-high field MR imaging offers marked gains in signal-to-noise ratio, spatial resolution, and contrast which translate to improved pathological and anatomical sensitivity. These benefits are particularly relevant for the neonatal brain which is rapidly developing and sensitive to injury. However, experience of imaging neonates at 7T has been limited due to regulatory, safety, and practical considerations. We aimed to establish a program for safely acquiring high resolution and contrast brain images from neonates on a 7T system. Methods: Images were acquired from 35 neonates on 44 occasions (median age 39 + 6 postmenstrual weeks, range 33 + 4 to 52 + 6; median body weight 2.93 kg, range 1.57 to 5.3 kg) over a median time of 49 mins 30 s. Peripheral body temperature and physiological measures were recorded throughout scanning. Acquired sequences included T2 weighted (TSE), Actual Flip angle Imaging (AFI), functional MRI (BOLD EPI), susceptibility weighted imaging (SWI), and MR spectroscopy (STEAM). Results: There was no significant difference between temperature before and after scanning (p = 0.76) and image quality assessment compared favorably to state-of-the-art 3T acquisitions. Anatomical imaging demonstrated excellent sensitivity to structures which are typically hard to visualize at lower field strengths including the hippocampus, cerebellum, and vasculature. Images were also acquired with contrast mechanisms which are enhanced at ultra-high field including susceptibility weighted imaging, functional MRI, and MR spectroscopy. Discussion: We demonstrate safety and feasibility of imaging vulnerable neonates at ultra-high field and highlight the untapped potential for providing important new insights into brain development and pathological processes during this critical phase of early life.

Data-driven motion-corrected brain MRI incorporating pose-dependent B0 fields.

PURPOSE: To develop a fully data-driven retrospective intrascan motion-correction framework for volumetric brain MRI at ultrahigh field (7 Tesla) that includes modeling of pose-dependent changes in polarizing magnetic (B0 ) fields. THEORY AND METHODS: Tissue susceptibility induces spatially varying B0 distributions in the head, which change with pose. A physics-inspired B0 model has been deployed to model the B0 variations in the head and was validated in vivo. This model is integrated into a forward parallel imaging model for imaging in the presence of motion. Our proposal minimizes the number of added parameters, enabling the developed framework to estimate dynamic B0 variations from appropriately acquired data without requiring navigators. The effect on data-driven motion correction is validated in simulations and in vivo. RESULTS: The applicability of the physics-inspired B0 model was confirmed in vivo. Simulations show the need to include the pose-dependent B0 fields in the reconstruction to improve motion-correction performance and the feasibility of estimating B0 evolution from the acquired data. The proposed motion and B0 correction showed improved image quality for strongly corrupted data at 7 Tesla in simulations and in vivo. CONCLUSION: We have developed a motion-correction framework that accounts for and estimates pose-dependent B0 fields. The method improves current state-of-the-art data-driven motion-correction techniques when B0 dependencies cannot be neglected. The use of a compact physics-inspired B0 model together with leveraging the parallel imaging encoding redundancy and previously proposed optimized sampling patterns enables a purely data-driven approach.